Location

Duration

Learn to visualise data with R!

Our Data Visualisation course with R will allow you to exploit the graphical capabilities of R and find different ways to explore your data sets. You
will learn how to produce a range of different graphs that will allow you to
present data in the most appropriate manner. You’ll learn about basic graph
types such as bar charts, scatter and box-whisker plots, as well as more
“exploratory” types such as scatter plot matrices and conditioning plots. You
will also learn how to alter basic graphical parameters, and thus customise
your graphics to best effect.

By the end of this course, you will have learnt:

Graphics basics

Exploratory graphs

Adding and appending to graphs

Customising graphical parameters

Saving and exporting graphics

Who should attend

Presentation of data and results is fundamentally important in making your analyses accessible to others. This course is for everyone who works with data and therefore needs to present data and results graphically.

Prerequisites

Some prior experience of using R would be useful, as you will focus on the graphical capabilities of R. You’ll need a laptop with R installed.

On site

We are very happy to put together a tailored on-site R Training workshop based on your specific requirements, and can customise the content to take into account your existing programming experience level and types of statistical analysis project to which you will be applying R.

Course syllabus

Graphics basics

This session covers the basics and shows you how to produce a range of standard chart types as well as various customisations.

Scatter plots

Bar charts

Box-whisker plots

Pie charts

R commands - boxplot, barplot, plot, pie, col, lty, cex, pch, bg

Exploratory graphs

This session covers some more specialist graph types, which are especially useful in data analysis.

"The pace of the course and the instructor's flexibility meant we were able to cover a lot of ground, all of which was directly relevant to our upcoming projects. A number of different Big Data solutions were explored."